HGF-MiLaG:与中后性别意识战略对话中的情感识别等级图形融合
Yihan Wang1, Rongrong Hao1, Ziheng Li1
1Sydney Smart Technology College, Northeastern University, Qinhuangdao Campus, Qinhuangdao 066004, China.
Sensors (Basel, Switzerland)
|February 26, 2025
概括
这项研究引入了一种新的等级图融合与中晚性别意识策略 (HGF-MiLaG) 进行对话情感识别 (ERC). 这种新的方法增强了对对话依赖关系和性别影响的理解,以提高人与计算机交互的准确性.
科学领域:
- 人与计算机的交互 (HCI)
- 人工智能 (AI) 是一种人工智能.
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 在对话中识别情绪 (ERC) 对于提高HCI用户体验至关重要.
- 现有的ERC方法难以捕捉复杂的对话依赖关系 (时间,扬声器,模式).
- 在ERC的多任务学习中,性别信息注入的影响仍未得到充分研究.
研究的目的:
- 为ERC提出一个新的等级图融合与中期晚期性别意识战略 (HGF-MiLaG).
- 为了有效地建模内部模式和间模式的扬声器依赖性和时间依赖性.
- 调查性别信息的最佳注射点,以提高ERC的绩效.
主要方法:
- 开发了一个层次融合图表,以捕捉复杂的对话依赖关系.
- 实施了一项中期晚期多层次的性别意识战略,以整合性别信息.
- 在IEMOCAP和MELD多式联络数据集上评估了HGF-MiLaG模型.
主要成果:
- 与现有的ERC方法相比,HGF-MiLaG模型显示出更高的性能.
- 层次图有效地捕捉了说话者和时间依赖.
- 中后期的性别意识战略优化了性别信息对情感识别的影响.
结论:
- HGF-MiLaG提供了一种更强大的方法来在对话中识别情绪.
- 提出的方法有效地模拟了复杂的对话动态和性别影响.
- 这项工作为HCI应用程序的多式联络情绪识别提供了重大进展.
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